from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-17 14:06:35.327609
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 17, Nov, 2020
Time: 14:06:38
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.1441
Nobs: 113.000 HQIC: -43.4348
Log likelihood: 1150.81 FPE: 5.69479e-20
AIC: -44.3163 Det(Omega_mle): 2.65483e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.810114 0.225168 3.598 0.000
L1.Burgenland 0.148118 0.094484 1.568 0.117
L1.Kärnten -0.321505 0.077796 -4.133 0.000
L1.Niederösterreich -0.006911 0.225372 -0.031 0.976
L1.Oberösterreich 0.289101 0.182961 1.580 0.114
L1.Salzburg 0.112004 0.092613 1.209 0.227
L1.Steiermark 0.051613 0.131321 0.393 0.694
L1.Tirol 0.165583 0.086342 1.918 0.055
L1.Vorarlberg 0.019168 0.086958 0.220 0.826
L1.Wien -0.226269 0.178318 -1.269 0.204
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.854508 0.290396 2.943 0.003
L1.Burgenland -0.024758 0.121855 -0.203 0.839
L1.Kärnten 0.348110 0.100333 3.470 0.001
L1.Niederösterreich 0.058272 0.290660 0.200 0.841
L1.Oberösterreich -0.225971 0.235963 -0.958 0.338
L1.Salzburg 0.163424 0.119442 1.368 0.171
L1.Steiermark 0.182518 0.169363 1.078 0.281
L1.Tirol 0.135501 0.111354 1.217 0.224
L1.Vorarlberg 0.176099 0.112149 1.570 0.116
L1.Wien -0.628235 0.229974 -2.732 0.006
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.349819 0.096496 3.625 0.000
L1.Burgenland 0.105226 0.040491 2.599 0.009
L1.Kärnten -0.024148 0.033340 -0.724 0.469
L1.Niederösterreich 0.121560 0.096584 1.259 0.208
L1.Oberösterreich 0.262732 0.078408 3.351 0.001
L1.Salzburg -0.000661 0.039689 -0.017 0.987
L1.Steiermark -0.058305 0.056278 -1.036 0.300
L1.Tirol 0.094685 0.037002 2.559 0.011
L1.Vorarlberg 0.147274 0.037266 3.952 0.000
L1.Wien 0.011882 0.076418 0.155 0.876
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.233110 0.115472 2.019 0.044
L1.Burgenland -0.003934 0.048454 -0.081 0.935
L1.Kärnten 0.037739 0.039896 0.946 0.344
L1.Niederösterreich 0.079630 0.115577 0.689 0.491
L1.Oberösterreich 0.353265 0.093827 3.765 0.000
L1.Salzburg 0.096200 0.047494 2.026 0.043
L1.Steiermark 0.192555 0.067345 2.859 0.004
L1.Tirol 0.026312 0.044278 0.594 0.552
L1.Vorarlberg 0.110171 0.044594 2.471 0.013
L1.Wien -0.123579 0.091446 -1.351 0.177
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.959411 0.246892 3.886 0.000
L1.Burgenland 0.045113 0.103599 0.435 0.663
L1.Kärnten -0.017370 0.085302 -0.204 0.839
L1.Niederösterreich -0.140801 0.247116 -0.570 0.569
L1.Oberösterreich 0.039747 0.200613 0.198 0.843
L1.Salzburg 0.049365 0.101548 0.486 0.627
L1.Steiermark 0.101681 0.143991 0.706 0.480
L1.Tirol 0.235470 0.094672 2.487 0.013
L1.Vorarlberg 0.025072 0.095348 0.263 0.793
L1.Wien -0.258671 0.195521 -1.323 0.186
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.185073 0.172728 1.071 0.284
L1.Burgenland -0.044228 0.072479 -0.610 0.542
L1.Kärnten -0.012913 0.059678 -0.216 0.829
L1.Niederösterreich 0.216366 0.172885 1.252 0.211
L1.Oberösterreich 0.389116 0.140351 2.772 0.006
L1.Salzburg -0.034221 0.071044 -0.482 0.630
L1.Steiermark -0.049270 0.100737 -0.489 0.625
L1.Tirol 0.194956 0.066233 2.943 0.003
L1.Vorarlberg 0.048009 0.066706 0.720 0.472
L1.Wien 0.118110 0.136789 0.863 0.388
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.361892 0.220188 1.644 0.100
L1.Burgenland 0.049249 0.092394 0.533 0.594
L1.Kärnten -0.084973 0.076075 -1.117 0.264
L1.Niederösterreich -0.157407 0.220388 -0.714 0.475
L1.Oberösterreich -0.115247 0.178914 -0.644 0.519
L1.Salzburg 0.001384 0.090565 0.015 0.988
L1.Steiermark 0.384985 0.128416 2.998 0.003
L1.Tirol 0.542047 0.084432 6.420 0.000
L1.Vorarlberg 0.212697 0.085035 2.501 0.012
L1.Wien -0.181018 0.174374 -1.038 0.299
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.318617 0.250783 1.270 0.204
L1.Burgenland -0.004754 0.105232 -0.045 0.964
L1.Kärnten -0.075579 0.086646 -0.872 0.383
L1.Niederösterreich 0.192720 0.251011 0.768 0.443
L1.Oberösterreich 0.032536 0.203775 0.160 0.873
L1.Salzburg 0.230169 0.103149 2.231 0.026
L1.Steiermark 0.129111 0.146260 0.883 0.377
L1.Tirol 0.059229 0.096164 0.616 0.538
L1.Vorarlberg -0.006196 0.096851 -0.064 0.949
L1.Wien 0.148235 0.198603 0.746 0.455
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.754685 0.137178 5.502 0.000
L1.Burgenland -0.018072 0.057562 -0.314 0.754
L1.Kärnten -0.011748 0.047395 -0.248 0.804
L1.Niederösterreich -0.085697 0.137302 -0.624 0.533
L1.Oberösterreich 0.264343 0.111464 2.372 0.018
L1.Salzburg 0.006347 0.056422 0.112 0.910
L1.Steiermark 0.001009 0.080004 0.013 0.990
L1.Tirol 0.078120 0.052602 1.485 0.138
L1.Vorarlberg 0.170802 0.052977 3.224 0.001
L1.Wien -0.141564 0.108635 -1.303 0.193
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.068932 -0.065100 0.197194 0.223959 0.027057 0.075954 -0.170028 0.071812
Kärnten 0.068932 1.000000 -0.085250 0.164892 0.031842 -0.166941 0.163631 -0.009098 0.246099
Niederösterreich -0.065100 -0.085250 1.000000 0.208038 0.017306 0.135733 0.071689 0.038551 0.354510
Oberösterreich 0.197194 0.164892 0.208038 1.000000 0.226912 0.264115 0.060513 0.044207 0.007679
Salzburg 0.223959 0.031842 0.017306 0.226912 1.000000 0.133541 0.027248 0.046855 -0.108416
Steiermark 0.027057 -0.166941 0.135733 0.264115 0.133541 1.000000 0.091355 0.102477 -0.219683
Tirol 0.075954 0.163631 0.071689 0.060513 0.027248 0.091355 1.000000 0.129929 0.075304
Vorarlberg -0.170028 -0.009098 0.038551 0.044207 0.046855 0.102477 0.129929 1.000000 0.045581
Wien 0.071812 0.246099 0.354510 0.007679 -0.108416 -0.219683 0.075304 0.045581 1.000000